Time series analysis of outpatient blood collection visits: Fluctuation patterns and nursing staff allocation...

This study aimed to explore the characteristics of outpatient blood collection center visit fluctuation and nursing workforce allocation based on a time series model, and the application effect was evaluated.

To enhance the efficiency of phlebotomy at the hospital outpatient window and improve patient satisfaction, the First Affiliated Hospital with Nanjing Medical University implemented a time series analysis model in 2024 to optimize nursing staff allocation. The management team was led by a head nurse of the outpatient blood collection department with extensive experience. It included one director of the nursing department, six senior clinical nurses, one informatics expert, and one nursing master's degree holder. Retrospective time-series data from the hospital's smart blood collection system (including hourly blood collection volumes and waiting times) were extracted between January 2020 and December 2023. Time series analysis was used to identify annual, seasonal, monthly, and hourly variation patterns in blood collection volumes. Seasonal decomposition and the Autoregressive Integrated Moving Average Model (ARIMA) were employed to forecast blood collection fluctuations for 2024 and facilitate dynamic scheduling. A comparison was conducted to evaluate differences in blood collection efficiency and patient satisfaction before (January-June 2023) and after (January-June 2024) implementing the dynamic scheduling model based on the time series analysis and forecasting.

Visit volumes showed periodicity and slow growth, peaking every second and third quarter of the year and daily at 8:00-9:00 a.m. and 2:00-3:00 p.m. The ARIMA model demonstrated a good fit (R ² = 0.692, mean absolute percentage error = 8.28 %). After adjusting the nursing staff allocation based on the fluctuation characteristics of the number of phlebotomy per hour in the time series analysis model, at the peak period of the blood collection window, at least three nurses, one mobile nurse and two volunteers were added. The number of phlebotomy per hour increased from 289.74 ± 54.55 to 327.53 ± 37.84 person-time (t = -10.041, P < 0.01), waiting time decreased from 5.79 ± 2.68 to 4.01 ± 0.46 min (t = 11.531, P < 0.01), and satisfaction rose from 92.7 % to 97.3 % (χ ² = 6.877, P < 0.05).

Based on the time series analysis method, it is helpful for nursing managers to accurately allocate human resources and optimize the efficiency of outpatient service resources by mining the special change rule of the outpatient blood collection window and predicting the future fluctuation trend.